Category Archives: Quantitative Value Investing

Benjamin Graham refined and changed many of his views at the end of his life in the 1970s.

Even though he was retired and surrounded by beautiful people and weather in California, he continued to conduct extensive research into the behavior of securities as an intellectual pursuit.

Reading some of his writings and interviews from the period, some have concluded that Graham abandoned his philosophy and embraced the efficient market hypothesis.

Here is a quote of his that led many to this conclusion:

“I am no longer an advocate of elaborate techniques of security analysis in order to find superior value opportunities. This was a rewarding activity, say, 40 years ago, when our textbook ‘Graham and Dodd’ was first published; but the situation has changed a great deal since then. In the old days any well-trained security analyst could do a good professional job of selecting undervalued issues through detailed studies; but in the light of the enormous amount of research now being carried on, I doubt whether in most cases such extensive efforts will generate sufficiently superior selections to justify their cost.”

This is a quote that efficient market types will often throw in the face of value investors. To paraphrase these people: “See, even Ben Graham thought this was all a bunch of nonsense! Shut up an buy an index fund, idiot!”

Reading these quotes, many value investors are left stung in disbelief. It’s like suddenly discovering that the Pope is an atheist, Mr. Miyagi was secretly helping the Cobra Kai, Picard collaborated with the Romulans, or that Johnny eventually put Baby in a corner.

The Truth

The truth is more nuanced. Yes, Ben Graham didn’t think detailed, individual security analysis was as useful as it was when he originally wrote the book in the 1930s. That doesn’t mean he gave up on the concept of value investing.

In fact, Graham did not agree with the efficient market crowd. He had this to say about them:

“They say that the market is efficient in the sense that there is no practical point in getting more information than people already have. That might be true, but the idea of saying that the fact that the information is so widely spread that the resulting prices are logical prices – that is all wrong, I don’t see how you can say that the prices made in Wall Street are the right prices in any intelligent definition of what right prices would be.”

The behavior of markets is, indeed, crazy. You have to be slightly brainwashed by the beautiful, peer-reviewed, academic work of the Church of Beta to think that prices are logical. Look at all of the insane bubbles that have plagued securities markets in the last few decades. Look at the nonsensical valuation of stocks in early 2009.

Look at the activity in multiple asset classes. Dotcom stocks, crypto, even housing. Look at the wild ride that the S&P 500 had in the 1990s and 2000s. Was the late ’90s run up rational? Was the hammering that stocks endured in 2008 logical or emotional?

It was all irrational, it was crazy. It wasn’t a market unemotionally weighing information. It was herds of professional investors reacting emotionally to events.

Mr. Market is alive and still doing crazy shit. If you don’t believe me, just watch the cable coverage of market action every day. Cable financial news is a torrent of speculation, FOMO, greed, and fear.

Another important snippet from the quote really stands out: “the information is so widely spread.” Graham was writing in the 1970s. We tend to think of the 1970s as a time when people were using stone tablets in between bong hits and classic rock albums. The thinking is that modern markets are so much better because we have the internet, computers, financial Twitter, blogs. We are so sophisticated and technologically advanced!

This is a conceit of every generation. Everyone thinks that their era is remarkably sophisticated and eras of the past were the dark ages. The experiences of our ancestors are primitive and not useful. The reality is that history rhymes and human nature never changes, no matter our level of technological sophistication. Eventually, the innovations of every era are ultimately discarded and regarded as quaint.

In reality, the critical information people needed to know about markets was available in the 1970s. Just because there is more information and it is more convenient in today’s world, it doesn’t make modern investors any more sophisticated or less emotional than the investors of yore. Indeed, the critical information about stocks has been widely available for a long time.

There is a perception that because stock screening technology and the information is readily available, that the edge for value investors has been eliminated. I think that’s bunk. Whether it was Moody’s manuals in the 1950s, Value Line in the 1970s, or stock screeners today – it has never been hard to find cheap stocks. What’s hard is actually buying them, not discovering them.

The source of returns in value investing has never been informational, it has been behavioral. It has been revulsion towards companies that are in trouble contrasted with starry-eyed love for companies that are making all the right movies.

There is a perception that the cheap stocks of past markets were diamonds in the rough. With technology, the thinking goes, those diamonds have been scooped up. Nothing could be further from the truth. Cheap stocks were always ugly stocks. The idea that there were cheap situations without any hair on them is a myth. The reason that cheap stocks outperform in historical analysis is becausethey were ugly. It was because they had problems.

The only thing that has changed is the methods of gathering that information.

Quantitative Value Investing

Back to the topic at hand.

While Ben Graham thought that detailed individual security analysis was a waste of time, he also believed that the efficient market theory was bunk.

Graham supported quantitative value investing. In other words, systematically purchasing portfolios of cheap stocks. Within the portfolio, some stocks would undoubtedly be value traps. As a group, however, they would generate returns that would beat the market.

Graham sums it up in this quote:

“I recommend a highly simplified strategy that applies a single criteria or perhaps two criteria to the price [of a stock] to assure that full value is present and that relies for its results on the performance of the portfolio as a whole — i.e., on the group results–rather than on the expectations for individual issues.”

In other words, he believed that investors should select a portfolio of cheap stocks and construct a portfolio of them to systematically take advantage of market inefficiency.

The first method, which Graham was most famous for, was purchasing stocks selling below their net current asset value. Graham referred to investing in net-net’s in the following fashion:

“I consider it a foolproof method of systematic investment—once again, not on the basis of individual results but in terms of the expectable group outcome.”

A crucial part of Graham’s quote is his point that the results of individual net-net’s are not dependable. Graham recommends buying a basket of them and allowing the portfolio to generate returns.

The problem with this approach is that they aren’t available in bulk frequently in the U.S. markets. As Graham pointed out, net-net’s should only be purchased as a portfolio. The only time that there are enough net-net’s to create a portfolio is in market meltdowns like the early 2000s or 2008-09.

I am eagerly anticipating the next decline so I can buy a portfolio of net-net’s.

Simple Graham: Low Price/Earnings & Low Debt/Equity

The next approach that Graham outlined was buying a portfolio of stocks with simultaneously low P/E ratios and low debt/equity. The great thing about this approach is that it is applicable in the United States outside of meltdowns, unlike the net-net approach.

This is the approach that I take with my own investments, albeit with other criteria (low price/sales, low EV/EBIT, high F-Scores, etc.) and qualitative analysis added to it.

Regarding price/earnings ratios, Graham recommended purchasing stocks that double the yield on a corporate bond. He suggested looking at the inverse of the P/E ratio, or earnings yield. A P/E of 10 would be a 10% earnings yield, for instance.

“Basically, I want to double the interest rate in terms of earnings return.”

“Just double the bond yield and divided the result into 100. Right now the average current yield of AAA bonds is something over 7 percent. Doubling that you get 14, and 14 goes into 100 roughly seven times. So in building a portfolio using my system, the top price you should be willing to pay for a stock today is seven times earnings. If a stock’s P/E is higher than 7, you wouldn’t include it.”

In other words, the value criterion was remarkably simple: a low P/E ratio.

Graham’s second criteria was a low debt/equity ratio.

“You should select a portfolio of stocks that not only meet the P/E requirements but also are in companies with a satisfactory financial position . . . there are various tests you could apply, but I favor this simple rule: a company should own at least twice what it owes. An easy way to check on that is to look at the ratio of stockholders’ equity to total assets; if the ratio is at least 50 percent, the company’s financial condition can be considered sound.”

Concerning portfolio management, Graham recommended holding onto the stock for either two years or a 50% gain. I think this is an important point: Graham never recommended holding shares forever. That’s Buffett’s approach. Graham, in contrast, suggested a high turnover portfolio: buy a large group of undervalued stocks, wait for them to return to a reasonable valuation, then sell and move on to the next situation.

Graham backtested this method going back to the 1920s and found that it generated a 15% rate of return over the long run.

Even with an exceptional 15% rate of return, the strategy underperformed at some key moments. In 1998, for instance, it lost 1.94%. The S&P 500 was up 28% that year. In 1999, it gained only 2.51%. The S&P 500 was up 21% that year.

There was similar underperformance in the Nifty 50 era. In 1971, the Graham strategy returned only 1.57%. The S&P 500 gained 14.31% that year.

I believe we are in a similar moment right now. Only time will tell if I am correct.

The Simple Ben Graham Screen

I run multiple screens, but I use Graham’s criteria as a cornerstone in my stock selection. Even if I am wrong in my analysis, I know that I am at least looking in the right neighborhood.

Here are ten stocks that currently meet Graham’s criteria for earnings yield and debt/equity:

I am not recommending that you go out and purchase any of these stocks. I am merely showing that even in a frothy market like the U.S. today, there are still opportunities which meet Ben Graham’s criteria.

Random

The source of Graham’s 1970s quotes featured in this blog post is The Rediscovered Benjamin Graham by Janet Lowe. The book is a collection of articles written about Graham, Congressional testimony, interviews, and articles written by Graham himself. Of particular interest are the bullish articles that he wrote in the early 1970s and early 1930s, discussing the deep undervaluation of American stocks. You can buy it here on Amazon. It’s a great read and gives you a clear perspective on how Graham’s thoughts evolved over time.

Captain Picard is coming back. I can’t express how much I am pumped about this. Here is Patrick Stewart explaining his enthusiasm for the role.

Turkey. Yes, I own the Turkey ETF. Fortunately, it is a small position. Here is an excellent article on the crisis.

I watched a random, weird, and goofy movie last night: The Final Girls. It’s a parody of ’80s slasher movies. If you’re familiar with all of the tropes of the genre and you’re in the mood for some lighthearted fun, I recommend it. If you’re not familiar with the genre, the jokes will probably not resonate.

Better Call Saul is back. If you’re not watching, you’re missing out. If you were a fan of Breaking Bad and aren’t watching this one, what the hell? As the series moves along, it is living up to its predecessor.

I could name a litany of research, books, blogs, articles all showing a simple conclusion. The conclusion is pretty basic: the less you pay for a stock relative to a fundamental metric (earnings, asset value, etc.) the more likely it is to outperform. It’s common sense and all of the research shows it works.

Much of this blog is about how these simple approaches work over the long run. Cheap stocks outperform expensive stocks. In this blog post, I performed a simple backtest against the effectiveness of different valuation metrics. My conclusion was pretty simple: every one of them works even though some work better than others.

Why does value work?

Every valuation ratio is a measure of the expectations. A low valuation implies that the market has low expectations about the prospects of the stock. Embedded in expectations are emotional and behavioral biases. Value investors exploit these emotional and behavioral biases.

Value investing “works” because other investors overreact to news because they are more emotional. That’s the essence of Ben Graham’s allegory about “Mr. Market.”

The expectations embedded in a valuation ratio tend to be false. In reality, a stock with low expectations is likely to exceed those expectations. A stock with high expectations is likely to disappoint. This is true at a macro level when looking at entire stock markets based on CAPE ratios, and it’s true at a micro level when looking at individual companies.

I like to think of a value stock as a C-average kid. All that kid needs to do to impress their parents is come home with a B on their report card. Their parents are going to be thrilled. That’s Gamestop right now. In contrast, a straight-A student that comes home with a B is going to be grounded. That’s Amazon right now. Valuation = expectations.

Why Does EBIT/EV Work?

Most research shows that EBIT/EV is the best valuation metric of all. My own above backtesting reflects this along with backtests and analysis performed by people much smarter than I.

It works for a few reasons. Like all the valuation metrics, the enterprise multiple captures low expectations. It goes further than a standard market cap metric for two other reasons: (1) Using enterprise value in the calculation brings the strength of the balance sheet into the valuation ratio. Enterprise values also reveal the actual size of the enterprise, as debt can sometimes dwarf market cap (this is something General Motors investors found out the hard way 10 years ago). (2) The further that you move up an income statement, the less likely that the accounting numbers are prone to manipulation. This is the reason that metrics like price/sales work better than price/earnings – it’s harder for an accountant to fake sales than it is for them to fake earnings.

How do we improve on valuation alone?

We know that low valuation metrics work and we know that EBIT/EV is the best metric of all. Joel Greenblatt sought to improve upon EBIT/EV in The Little Book That Beats the Market. He added his own quality metric: return on invested capital (ROIC). He showed that the combination delivers impressive results.

However, separate research from Tobias Carlisle in Deep Value and James Montier in The Little Note that Beats the Market shows that the “quality” component actually brings performance down.

The question is: why doesn’t return on invested capital work?

Quite naturally, a company earning high returns on invested capital will attract competition. A high ROIC is a target on a company’s back. It attracts competitors, which over the long run depresses ROIC. In contrast, a low ROIC implies that competitors are leaving the industry. The industry is likely near a cyclical trough and is about to rebound. The goal of the deep value investor is to identify these moments and buy.

Warren Buffett emphasizes high ROIC, but the key to his success is that he can identify businesses with sustainable high ROIC. In other words, companies that can maintain a high ROIC over decades. Moreover, he recognizes these businesses when their price offers a margin of safety. This is a skill that hardly any other investors have been able to duplicate. You should be skeptical of anyone who claims they can identify these companies.

ROIC is probably useless for average investors because, unlike Buffett, we don’t have the ability to determine whether or not a company can sustain it.

In that case, if ROIC doesn’t work for the average investor, then what metric improves on merely buying cheap?

Graham’s preferred metric was the debt/equity ratio. In 1976, Graham recommended buying baskets of 30 companies that have a simultaneously low P/E ratios and a low debt/equity ratio. His backtesting revealed that this strategy returned 15% per year.

My backtesting reveals that Graham (as usual) is right. A low debt/equity ratio improves the performance of every single valuation ratio and reduces maximum drawdowns.

My backtest only goes back to 1999, the universe is the S&P 1500, the portfolios are rebalanced annually, and the portfolio size is 30 stocks. The results are below.

As you can see, merely restricting the backtest to a population of companies with a debt/equity ratio below 50% (i.e., they have triple the assets that they have in debts) improves every single valuation metric that I tested. It seems absurd that such a simple metric would vastly improve the performance of every valuation metric, but that’s the result.

Why is this the case?

I think it is because any company whose stock has a cheap valuation is going to be in some type of trouble. The strength of a balance sheet is the reason that a company survives the crisis that it is mired in.

For most value stocks, all that they need to do to thrive is merely survive. There is nothing that guarantees survival more than a strong balance sheet. Usually, these companies are in an industry that is going through a difficult time (like retail right now). When the industry is going through a tough time, competitors go out of business or leave the industry voluntarily. When the competition is gone, the stage is set for the industry to come back to life. When the industry comes back to life, the survivors reap the rewards.

In the retail sector right now, the casualties are going to be the highly leveraged firms. An excellent example of this is Sears. Sears currently has negative equity and is highly leveraged (debts exceed assets). The Sears balance sheet is probably not strong enough to survive the “retailpocalypse”. In contrast, a company like Foot Locker (one of my holdings) has a strong balance sheet and will likely survive the shakeout. There is no way to know for sure, but common sense tells me that this is the likely outcome.

The survivors of an industry decline will have plenty of reasons for why they survived: Our management is excellent, our product is better, we have strategic vision, our employees are just so damn good, blah blah blah MBA buzzwords.

The real reason the company survived is that it had a stronger balance sheet than everyone else going into the downturn. The balance sheet made the company survive the tough time and hang in there longer than everyone else — in other words, a company with a good balance sheet can survive. As Rocky Balboa might have put it, the company can “go the distance”. In battered industry going through a crisis, all that a company needs to do to win is go the distance and survive.

Prior to reading Deep Value by Tobias Carlisle, I always thought that the key in value investing was to find cheap companies that could grow fast.

Buffett even discussed the merits of combining growth and value in his 1992 letter:

Most analysts feel they must choose between two approaches customarily thought to be in opposition: “value” and “growth.” Indeed, many investment professionals see any mixing of the two terms as a form of intellectual cross-dressing.

We view that as fuzzy thinking (in which, it must be confessed, I myself engaged some years ago). In our opinion, the two approaches are joined at the hip: Growth is always a component in the calculation of value, constituting a variable whose importance can range from negligible to enormous and whose impact can be negative as well as positive.

A key point of Tobias’ book is that growth is not how value delivers returns. The discount from intrinsic value and the closing of that gap is the key driver of return in a value portfolio.

De Bont & Thaler

He cites two studies in the book that are compelling because of how counterintuitive they are. They were both conducted by Werner De Bondt and Richard Thaler. The first study looked at the best-performing stocks and compared them to the worst performing stocks in terms of price performance. They found that the worst performers go on to outperform the best by a substantial margin.

They also looked at this in terms of fundamental earnings growth. They reached the same conclusion: the worst companies outperform the best.

A Simple Backtest

I decided to backtest more recent data myself to see if this still holds true. In an S&P 500 universe, I performed a backtest going back to 1999. I compared the performance of the 30 companies with the fastest growth in earnings per share to a portfolio of the 30 worst stocks, rebalanced annually.

Just as De Bont & Thaler determined before, the 30 worst companies continue to outperform the 30 best.

Keep in mind: there is no other factor involved here except for 1-year earnings growth. We’re not even looking at these stocks in a value universe: it’s simply the 30 fastest growers vs. the 30 worst.

Value Drives Returns

The evidence suggests that value alone is the best determinant of future returns. Growth isn’t nearly as powerful.

For instance, I also tested the performance of a universe of stocks with a P/E less than 10. This universe of stocks delivered an 11.69% rate of return since 1999. Within this winning universe, if you bought the 20 stocks with the best earnings per share growth then the return actually declined to 9.77%. Fast growing value stocks actually underperform the overall value universe.

The same is also true from a macro standpoint. Looking at the performance of the S&P 500 since the 1950s, the greater determinant of future returns is starting valuation, not actual business performance.

As you can see, the valuation of the market at the start of the decade (Shiller CAPE and the average investor allocation to equities – both valuation metrics are discussed in this blog post) are far more predictive of future returns than actual business performance.

Look at where the divergence is widest — the 1980s versus the 2000s.

The 2000s was a much better decade than the 1980s in terms of actual business performance. During the 2000s, earnings grew by 191%. In the 1980s, earnings only grew by 16.40%.

However, the 1980s witnessed a 409% total return for the S&P 500, while the 2000s actually clocked in a net decline of 9%.

Of course, there were macro events driving both markets. In the 1980s, returns were bolstered by interest rates declining from all-time highs once inflation was brought under control. At the end of the 2000s, we suffered the worst financial crisis since the Great Depression and the worst recession since the early 1980s, which negatively impacted stocks at the end of the decade.

With that said, the key factor behind the returns was the overall valuation of the market, not macro events or even business performance.

The undervaluation of the US market in the early 1980s was the true force that propelled the bull market forward. In 1980, the Shiller CAPE for the US market was 8.85 and the average investor allocation to equities was only 23%.

In contrast, the overvaluation of the US market in 2000 was the key force that drove down returns over the next decade. In 2000, the Shiller CAPE was 43.77 and the average investor allocation to equities was 50.84%. Actual corporate results were impressive but that wasn’t enough. Valuation mattered more.

Conclusion

The conclusion is both simple and radically counterintuitive: valuation matters more than growth in predicting future returns for a single company stock or an entire market.

In an earlier post, I examined the performance of different value metrics. My conclusion was simple: cheap stocks beat expensive stocks.

Most value investors don’t simply look for cheap alone. They try to find companies that are both cheap and good. Good is typically defined as companies that can earn high returns on their capital.

Finding these companies is a worthwhile pursuit but it is difficult to pull off systematically because companies earning high returns on capital are going to attract significant competition. I think it is far more difficult to do this than most value investors appreciate. Mean reversion, fueled by competition, inevitably pulls these returns down. Finding the rare birds that don’t succumb to this is hard. These companies usually have a “moat“, which is hard to identify. Needless to say, this kind of investing requires a touch of genius that I don’t have. When investing, I operate under the assumption that every company succumbs to mean reversion.

With that said, finding these rare opportunities certainly pays off over the long run. You can park money in a company like Coca-Cola or Nike and earn high returns over long stretches of time, while reducing taxes and transaction costs.

The Magic Formula

Joel Greenblatt sought out a systematic quantitative method to find companies that are simultaneously cheap and earn high returns on capital. The result was The Little Book that Beats the Market. In the book, Greenblatt demonstrated that simultaneously buying cheap companies that earn high returns on invested capital will outperform. He calls this the magic formula and generously maintains a free screener here.

Tobias Carlisle took this a step further in Deep Valueand discovered that the quality metric of high returns on invested capital actually reduces returns from the magic formula. He explains in Deep Value how mean reversion tends to bring these returns down. Cheap alone is better than cheap plus good. In other words, it takes qualitative insight to determine which companies have a moat that will allow them to sustain high returns on capital. Tobias maintains a free large cap screen for this here.

I would recommend reading both The Little Book That Beats the Market and Deep Value.

Backtesting Quality Metrics

I decided to test the returns for myself and try to see which “quality” metric works best when combined with a value factor. The test I ran is limited to Russell 3000 components. My definitions of cheapness were:

EBITDA/Enterprise Value is higher than 20%

Price to free cash flow is less than 15

Price to sales is less than 1

Earnings Yield is over 10% (i.e., P/E is less than 10)

Price to book less than 1

Price to tangible book less than 1

In addition to examining metrics that define high returns on capital, I also included metrics for financial quality, such as the debt to equity ratio and the Piotroski F-Score. Below is a summary of all of the quality metrics that I tested.

Return on Equity – This is the oldest and most simple method of corporate quality. It is simply the company’s net income divided by equity (assets – liabilities). For the purposes of the test, I define high ROE as over 20%.

Return on Invested Capital – Joel Greenblatt’s preferred measure of quality. This is earnings before interest and taxes divided by invested capital. Invested capital is defined as working capital plus net fixed assets. For the purposes of the test, I define high ROIC as over 15%.

Gross Profits/Assets – Robert Novy-Marx created a very simplistic measure of quality, gross profits/assets. He found that this method works extremely well, because it uses profits further up of the income statement where it is more difficult for a firm to manipulate the numbers. You can read his paper on the subject here. For the purposes of the test, I define good gross profitability as over 30%.

Debt/Equity – This is another simplistic measure of financial quality. It is simply total debt divided by equity (assets-liabilities). For the purposes of the test, I define a good debt/equity ratio as under 50%.

F-Score – This is a more complex measure of financial quality designed by Joseph Piotroski, who is currently a professor at Stanford. Piotroski designed a 9 point scale of financial quality in a paper written back in 2000. Piotroski backtested combining this measure of financial quality with price to book and found that the results greatly exceeded the market. Each component adds to the score. A perfect F-Score would be a 9. It’s too bad F-Scores don’t go up to eleven. The components of the F-Score are defined below.

A net decline in long-term debt for the current year.

A net increase in the current ratio in the current year. The current ratio is current assets/current liabilities. It measures the liquidity of the company’s balance sheet to meet short-term obligations.

A positive increase in gross margins in the current year.

Faster asset turnover in the current year.

The total number of shares outstanding is flat or decreasing. In other words, the company isn’t issuing new equity and diluting the current pool of shares.

Return on assets is positive.

Operating cash flow is positive.

Return on assets for the current year is higher than the previous year.

Operating Cash Flow/Total Assets is higher than return on assets.

The results of the backtest are below. The results are in the Russell 3000 universe with annual rebalancing beginning in 1999.

The High Return Metrics – ROE, ROIC, GP/Assets

Measures for returns on capital – ROE, ROIC and GP/Assets – actually detract from the performance of EBITDA/Enterprise Value. They add performance to the other valuation metrics slightly, with Gross Profits/Assets being the best.

The middling performance of high return quality metrics is due to mean reversion, or the propensity for high return businesses to eventually succumb to the pressures of competition.

With that said, if you are trying to identify high return businesses, the best metric to use appears to be Gross Profits/Assets.

Financial Quality (The Debt/Equity Ratio and the F-Score)

In contrast, measures of financial quality, such as the debt/equity ratio and the F-Score, supercharge all of the valuation metrics that are examined here. Why is this?

Cheap stocks are only cheap because they are in some kind of trouble. There is an “ick” factor. Any time you run a value screen, you will scratch your head and think “Do I really want to invest in this garbage?”

This is why financial quality metrics are more useful than business quality metrics. If a company has a good balance sheet and is financially healthy then it has time to resolve its problems. Managers have time to implement a new strategy that can turn things around. Even without a new strategy, time will help the financially healthy company. For instance, if the company is in a crowded, competitive industry, the financially healthy company can weather the storm while the highly leveraged firms will go out of business first. Less firms means less competition. Less competition means that future returns in the industry will improve.

This is the essence of the simple Graham method that I follow with my own portfolio. I am looking for cheap companies that have the financial ability to weather the storm that they’re in.

This is also why the high return metrics add a little to the other valuation metrics but detract from the EBITDA yield. Unlike the other valuation metrics tested here, the EBITDA yield is the only one here that uses Enterprise Value in the calculation. Enterprise Value brings balance sheet health into the valuation ratio. For EBITDA/Enterprise Value to result in a high yield, the company must have little debt and a lot of cash on hand. In other words, valuation metrics that use Enterprise Value will identify companies that are both cheap and have safe balance sheets.

RadioShack vs. Best Buy

This reminds me of an article I read over at the Motley Fool. The author explains why Radio Shack fell apart and Best Buy was able to turn around.

Radio Shack had numerous problems, including asking for your phone number when you buy batteries.

Best Buy’s problem is that it basically became a showroom for Amazon. The referenced article explain how Best Buy successfully turned things around by cutting costs, emulating the Apple Store, expanding the Geek Squad and improving their website.

I have a more simplistic explanation for why Best Buy was able to recover and Radio Shack fell apart. Radio Shack had a lot of debt and Best Buy didn’t. Radio Shack’s debt to equity ratio was over 670%. Best Buy’s debt to equity ratio was 41% a few years ago and is 29% today.

Best Buy’s balance sheet gave them an edge: time. They had time to work through their problems and try to find a solution. If Best Buy had Radio Shack’s debt levels, the CEO would have never been able to pursue the turnaround strategy. All troubled companies are trying to turn things around, but only those with financial strength will have the time to do so.

Screening for High F-Scores and EBITDA Yield

One of the most robust combinations tested was the F Score and the EBITDA Yield, with a 17.69% rate of return since 1999. I ran a screen for companies with an EBITDA yield over 20% and an F-Score of 8 or higher. This combination of criteria is very stringent. It only returned 5 results out of the entire Russell 3000. Best Buy actually comes up in this screen, implying that it is still a financially healthy bargain. Output from the screen is below.

I am not going to buy positions in these companies, but wanted to share the results of what I found. Hopefully this will give you some useful leads that are worthy of further research.

Discounted cash flow (DCF) analysis is the most popular method of business valuation. It is taught extensively in most finance classes. The goal is to find a reasonable price for a future stream of cash flows and compare it to a risk-free rate of return, usually US treasuries.

It’s also fraught with peril because it usually results in overvaluing businesses. It is the preferred method of valuation in investment banking. I suspect this is because investment bankers can easily game the numbers and make companies appear more valuable than they actually are. Allow me to explain.

Apple Valuation (AAPL)

To show the power of assumptions, let’s try a real world valuation example. Let go with Apple (AAPL) using this method.

At the end of the 2016 fiscal year, there were 5.336 billion shares of Apple common stock.

$52.296/5.336 = $9.80 of free cash flow per share of Apple stock.

So what’s the value of $9.80 in discounted cash flow? Let’s use DCF analysis to figure it out.

Zero Growth Example

For example 1, let’s take an extreme approach. Let’s say Apple won’t grow at all (unlikely). For the interest rate, we’ll use US treasuries. The 10-year US treasury currently pays 2.38%. Here is a link with a detailed explanation of the math.

If you want to do this quickly, Gurufocus has a calculator tool that you can use here. Another nice shortcut is a formula that can be used in Microsoft Excel or in Google sheets. Simply input the following formula into a cell:

=NPV(discount rate, cash flow 1, cash flow 2, etc.)

Now, let’s try to find the present value of a $9.80 stream of cash flows. Based on no growth and 10 years of cash flows and using the 2.38% rate, we get a value for Apple of $86.30.

Growth and Different Discount Rates

2% Growth, 2.38% rate, 10 years = $96.02

What if instead of using the 10 year treasury as our base, we used the 10 year average AAA corporate bond yield, 2.96%

2% Growth, 2.96% rate, 10 years = $93.11

Terminal value

Most likely, Apple isn’t going to go out of business in 10 years. For this reason, most DCF analysis adds a terminal value to the value after the 10 years of cash flows. Let’s proceed with our assumption that there will be 2% growth for 10 years, then let’s say after 10 years the growth rate drops to 1%.

Present value of 10 years of cash flows + Terminal Value = $173.52

Now, what if we increased our assumptions? Let’s say Apple grows by 5% a year, and then the terminal value grows earnings at 3% into the future? Now the value goes up to $228.54!

With DCF analysis, you can make the data say whatever you want. That’s great for investment bankers but it’s not very good for investors.

Conclusion

By messing around with different assumptions, I produced valuations for Apple that ranged from $86.30 to $228.54. All of these assumptions are debatable. You can’t say with any degree of certainty where interest rates are going, what Apple’s cost of capital will be, what their growth rate will be, how long the business will be viable, etc. All of these are assumptions. Also keep in mind that I produced this wide range of values with one of the the largest and most recognizable company in the United States. If we can’t safely value Apple, how can we safely value a micro-cap stock?

For this reason, I avoid discounted cash flow analysis. It is simply too easy to twist around the data with your assumptions and get the result you want. If you want to find a margin of safety with DCF analysis, you’re going to find one. I suspect that this is what the investment banking community does when they want to convince corporate managers to make acquisitions that may not be in the best interests of the acquirer.

A simple ratio (i.e., the stock trades at 10 times earnings) is a far more simplistic . . . and far more telling . . . statistic than DCF analysis. The cheapness of something should hit you over the head and should be abundantly obvious. If it’s not, move onto something else. There are plenty of publicly traded companies. Torturing the data to get the result you want is not a prudent path.

I prefer the Graham approach and focus on what’s actually known in the here and now without making so many assumptions about the future. This is why the Grahamian balance sheet approach (because what’s more clear cut than the value of a balance sheet?) of net-net’s is a nearly foolproof method of investment.

For the goal of finding the present value of cash flows in analysis of stocks, I think a more useful metric is one that is more simple: price to free cash flow or enterprise value to free cash flow. If you can find a decent company like Apple trading at a price to free cash flow of 10 or less, then DCF analysis would likely yield a number close to the current price even with very conservative assumptions. It is probably then a good candidate worthy further research.

In the world of finance, there is a tendency to make things more complicated than they really are. I like to keep things simple.

PLEASE NOTE: The information provided on this site is not financial advice and I am not a financial professional. I am an amateur and the purpose of this site is to simply monitor my successes and failures. Full disclosure: my current holdings.

Value investors use a number of ratios to assess whether a stock is cheap. Everyone has their favorite. Everyone debates the merits of one versus the other. I backtested some of the popular ratios to see how they would perform if you simply split the market up into deciles and compared the cheapest to the most expensive deciles. The population I used for this analysis was the S&P 1500. In this case, we are comparing the most expensive 150 stocks to the cheapest 150 stocks. These are the total returns since 1999, with the 150 stocks re-balanced annually, because re-balancing monthly is impractical.

The Enterprise Value is the total cost of the firm to an acquirer. Enterprise values are the total cost of the firm to an acquirer at the current market price. In other words, if you were to buy this company in its entirety, you wouldn’t simply pay the market price. You would also assume all of its debt obligations and would inherit all of its cash on hand. It is the cost to acquire the entire company. EV gives you a good idea of the true size of the business. The calculation for Enteprise Value is:

Price/Cash Flow – The price per share divided by the total trailing twelve month cash flow for the last calendar year. In other words, how much total cash is the stock generating for what you are paying?

Price/Sales – The price per share divided by the total revenue per share. This ratio was popularized by Kenneth Fisher in his 1984 book Super Stocks.

Price/Free Cash Flow – Free cash flow is the company’s operating income minus its capital expenditures. Free cash flow strips away the company’s other financial performance variables and looks simply at how the core business is doing. This ratio looks at how much free cash flow is being generated per share relative to the price of the stock.

Free Cash Flow/Enterprise Value – This is the same thing as price/free cash flow, but instead compares free cash flow to the total size of the business.

Price/Book Value – Book value is the total balance sheet value of the company. It’s the simple equation Assets – Liabilities = Shareholder Equity. The goal of many asset-based value investors is to buy company’s that are trading at or below book value.

Price/Earnings – This is the most basic and common valuation metric. It takes the per share price of the stock and divides it by the earnings per share.

Price/Tangible Book Value – The same thing as price/book value, with a twist. When calculating shareholder equity, intangible assets are taken out of total assets. The goal here is to look at what assets can actually be sold and turned into cash.

The results are below:

I think it is best to look at the effectiveness of the ratio based on the difference in return between the cheapest and most expensive decile, rather than looking at its total return for the cheapest decile. A ratio that is effective in identifying cheap stocks should be equally effective in identifying expensive stocks. Based on the backtesting, the acquirer’s multiple popularized by Tobias Carlisle is the most effective. Tobias maintains a nice screener here.

Here is a visualization of the value premium in chart form:

The important takeaway is that no matter which ratio you prefer, they all work to some extent and buying expensive stocks is a risky bet. Value investors can debate about which ratio works best, but they all work! No matter how you slice it, cheap beats expensive.